Real-Time Locomotion Recognition Algorithm for an Active Pelvis Orthosis to Assist Lower-Limb Amputees

Abstract

Powered hip exoskeletons, in combination with passive prostheses, have been recently proposed to improve the economy and pattern of walking of lower-limb amputees within clinical scenarios. However, for everyday life support, a real-time control strategy that can accurately recognize different locomotion modes and transitions is required. In this letter, we proposed a novel locomotion recognition algorithm for an Active Pelvis Orthosis designed to assist people with lower-limb amputation, in quasi-static (sit-to-stand/stand-to-sit) and dynamic locomotion modes (walking and stairs negotiation). Two finite-state machines were combined to recognize in real-time the participants’ locomotion, one was a rule-based algorithm and one was based on four linear discriminant analysis classifiers. Four transfemoral amputees took part in the experiments and performed a circuit of tasks in two conditions, namely in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transparent mode</i> (the exoskeleton was controlled to provide null output impedance), and in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">assistive mode</i> (the exoskeleton was controlled to output an assistive torque consistently with the locomotion mode recognized by the algorithm), to test the algorithm in real-time conditions. The median (25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> , 75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile) between-subjects recognition accuracy was 94.8% (93.4%, 96.5%) with user-dependent models. Offline analysis on user-independent models with leave-one-subject-out validation resulted in between-subjects recognition accuracy equal to 95.9% (94.0%, 97.8%). The results of this study pave the way for future experimentations of the technology in ecological scenarios.